{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f5d39829-40a5-4cd5-90d3-958d10ba8a23",
   "metadata": {},
   "source": [
    "# load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b39a6b-9578-42a6-8ca8-3af21669b046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income ocean_proximity  \n",
       "0       322.0       126.0         8.3252        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014        NEAR BAY  \n",
       "2       496.0       177.0         7.2574        NEAR BAY  \n",
       "3       558.0       219.0         5.6431        NEAR BAY  \n",
       "4       565.0       259.0         3.8462        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "housing_X, housing_y = fetch_openml(name=\"california_housing\",version=1,return_X_y=True, as_frame=True)\n",
    "housing_X.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3881a0eb-fc15-4fef-944c-935fb78606bf",
   "metadata": {},
   "source": [
    "# data overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "69dfa9f4-843b-41e6-81ee-6529f3d10c9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000  \n",
       "mean       537.870553   1425.476744    499.539680       3.870671  \n",
       "std        421.385070   1132.462122    382.329753       1.899822  \n",
       "min          1.000000      3.000000      1.000000       0.499900  \n",
       "25%        296.000000    787.000000    280.000000       2.563400  \n",
       "50%        435.000000   1166.000000    409.000000       3.534800  \n",
       "75%        647.000000   1725.000000    605.000000       4.743250  \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a661e9-4542-4b06-899b-9dbfdcd1f852",
   "metadata": {},
   "source": [
    "# data munging\n",
    "## missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e698abd-b02a-413c-89c2-a0cc556bb7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_X.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5292e8b9-aa19-4421-b650-f31681b6595d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'total_bedrooms'}>]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_bdr_distribution = pd.DataFrame(housing_X.loc[~housing_X.total_bedrooms.isna()])\n",
    "total_bdr_distribution.hist(column=[\"total_bedrooms\"],bins=50,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80ac522d-783e-4353-ae9b-9cd5d7586d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>536.838857</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>419.391878</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>643.250000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>39320.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \n",
       "count    20640.000000  20640.000000  20640.000000   20640.000000  \n",
       "mean       536.838857   1425.476744    499.539680       3.870671  \n",
       "std        419.391878   1132.462122    382.329753       1.899822  \n",
       "min          1.000000      3.000000      1.000000       0.499900  \n",
       "25%        297.000000    787.000000    280.000000       2.563400  \n",
       "50%        435.000000   1166.000000    409.000000       3.534800  \n",
       "75%        643.250000   1725.000000    605.000000       4.743250  \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_X.total_bedrooms.fillna(value=housing_X.total_bedrooms.median(),axis=0,inplace=True)\n",
    "housing_X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd450681-7b97-428c-90fb-be833b5b78f2",
   "metadata": {},
   "source": [
    "## one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ce7b07fc-2a2d-4fc8-a151-cc68e31a492d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer, LabelEncoder\n",
    "\n",
    "# try two different encoding methods for future model selection\n",
    "# oh will use one-hot encoding\n",
    "housing_X_oh = housing_X.copy()\n",
    "# lab will use label encoding\n",
    "housing_X_lab = housing_X.copy()\n",
    "lab_encoder = LabelEncoder()\n",
    "oh_encoder = LabelBinarizer()\n",
    "label = lab_encoder.fit_transform(housing_X.ocean_proximity)\n",
    "one_hot = oh_encoder.fit_transform(housing_X.ocean_proximity)\n",
    "housing_X_oh.drop(columns=[\"ocean_proximity\"],inplace=True)\n",
    "housing_X_oh= pd.concat([housing_X_oh, pd.DataFrame(one_hot,dtype=np.float64)],axis=1)\n",
    "housing_X_lab.ocean_proximity = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d4a4bd9-9168-4ccd-ba66-9a25f581fd73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['<1H OCEAN' 'INLAND' 'ISLAND' 'NEAR BAY' 'NEAR OCEAN']\n"
     ]
    }
   ],
   "source": [
    "print(oh_encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "284557f7-f15d-46b6-8bdf-88cdb18b3343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  CLOSE OCEAN  INLAND  ISLAND  \\\n",
       "0       322.0       126.0         8.3252          0.0     0.0     0.0   \n",
       "1      2401.0      1138.0         8.3014          0.0     0.0     0.0   \n",
       "2       496.0       177.0         7.2574          0.0     0.0     0.0   \n",
       "3       558.0       219.0         5.6431          0.0     0.0     0.0   \n",
       "4       565.0       259.0         3.8462          0.0     0.0     0.0   \n",
       "\n",
       "   NEAR BAY  NEAR OCEAN  \n",
       "0       1.0         0.0  \n",
       "1       1.0         0.0  \n",
       "2       1.0         0.0  \n",
       "3       1.0         0.0  \n",
       "4       1.0         0.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_X_oh.rename(columns={0:'CLOSE OCEAN' ,1:'INLAND',2:'ISLAND',3:'NEAR BAY' ,4:'NEAR OCEAN'},inplace=True)\n",
    "housing_X_oh.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1099f025-6366-4eaf-9122-748760dbad90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  ocean_proximity  \n",
       "0       322.0       126.0         8.3252                3  \n",
       "1      2401.0      1138.0         8.3014                3  \n",
       "2       496.0       177.0         7.2574                3  \n",
       "3       558.0       219.0         5.6431                3  \n",
       "4       565.0       259.0         3.8462                3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_X_lab.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364f9d2a-801a-44f5-ad47-608074949a94",
   "metadata": {},
   "source": [
    "## pearson r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "53ba944a-6e2e-4825-805a-03fda630f4be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attr_pearson</th>\n",
       "      <th>attr_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>0.688075</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <td>0.256617</td>\n",
       "      <td>1.131210e-307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR BAY</th>\n",
       "      <td>0.160284</td>\n",
       "      <td>7.939217e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <td>0.141862</td>\n",
       "      <td>3.037437e-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.134153</td>\n",
       "      <td>1.689385e-83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>0.105623</td>\n",
       "      <td>2.761861e-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.065843</td>\n",
       "      <td>2.823421e-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.049457</td>\n",
       "      <td>1.167146e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISLAND</th>\n",
       "      <td>0.023416</td>\n",
       "      <td>7.672192e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>-0.024650</td>\n",
       "      <td>3.976308e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>3.923322e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.144160</td>\n",
       "      <td>2.939859e-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INLAND</th>\n",
       "      <td>-0.484859</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    attr_pearson         attr_p\n",
       "median_income           0.688075   0.000000e+00\n",
       "CLOSE OCEAN             0.256617  1.131210e-307\n",
       "NEAR BAY                0.160284  7.939217e-119\n",
       "NEAR OCEAN              0.141862   3.037437e-93\n",
       "total_rooms             0.134153   1.689385e-83\n",
       "housing_median_age      0.105623   2.761861e-52\n",
       "households              0.065843   2.823421e-21\n",
       "total_bedrooms          0.049457   1.167146e-12\n",
       "ISLAND                  0.023416   7.672192e-04\n",
       "population             -0.024650   3.976308e-04\n",
       "longitude              -0.045967   3.923322e-11\n",
       "latitude               -0.144160   2.939859e-96\n",
       "INLAND                 -0.484859   0.000000e+00"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "attrs = []\n",
    "attr_ps = []\n",
    "for col in housing_X_oh.columns:\n",
    "    attr, p = pearsonr(housing_X_oh.loc[:,col],housing_y)\n",
    "    attrs.append(attr)\n",
    "    attr_ps.append(p)\n",
    "attrs = pd.DataFrame(data={\"attr_pearson\":attrs,\n",
    "                           \"attr_p\":attr_ps},\n",
    "                     index=housing_X_oh.columns)\n",
    "attrs.sort_values([\"attr_pearson\"],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16726d37-baea-4166-9dd1-8ec014d19e91",
   "metadata": {},
   "source": [
    "## JS Divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae73ad5b-99d6-42ec-8017-e8e05a547099",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import entropy\n",
    "\n",
    "def js_divergence(p,q):\n",
    "    M = (p + q)/2\n",
    "    js = 0.5 * entropy(p,M) + 0.5 * entropy(q,M)\n",
    "    return np.round(float(js),6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32dcc3f9-4493-48bc-8584-61a3b3a567a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>js_attr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ISLAND</th>\n",
       "      <td>3.867678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR BAY</th>\n",
       "      <td>1.046660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <td>0.995093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INLAND</th>\n",
       "      <td>0.888202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <td>0.379044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.184248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>0.171957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.165328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.156156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>0.120208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.081588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0.077515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>0.039420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     js_attr\n",
       "ISLAND              3.867678\n",
       "NEAR BAY            1.046660\n",
       "NEAR OCEAN          0.995093\n",
       "INLAND              0.888202\n",
       "CLOSE OCEAN         0.379044\n",
       "population          0.184248\n",
       "total_bedrooms      0.171957\n",
       "households          0.165328\n",
       "total_rooms         0.156156\n",
       "housing_median_age  0.120208\n",
       "latitude            0.081588\n",
       "longitude           0.077515\n",
       "median_income       0.039420"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "js_attrs = []\n",
    "for col in housing_X_oh.columns:\n",
    "    attr = js_divergence(housing_X_oh.loc[:,col],housing_y)\n",
    "    js_attrs.append(attr)\n",
    "js_attrs = pd.DataFrame(data={\"js_attr\":js_attrs},index=housing_X_oh.columns)\n",
    "js_attrs.sort_values([\"js_attr\"],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb6437c-87d4-4b37-add7-44eaf1e5695d",
   "metadata": {},
   "source": [
    "## create new attributes\n",
    "1. delete longitude 2(island)\n",
    "2. bdr_per_rm\n",
    "3. rm_per_household\n",
    "4. income_per_pp\n",
    "5. delete households population total_bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "86bb3f2d-2178-4fa9-b8af-bad7bdfd4b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>median_income</th>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <th>income_per_pp</th>\n",
       "      <th>rm_per_household</th>\n",
       "      <th>bdr_per_rm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025855</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>0.146591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.155797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014632</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>0.129516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010113</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>0.184458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006807</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>0.172096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  median_income  \\\n",
       "0    -122.23     37.88                41.0        880.0         8.3252   \n",
       "1    -122.22     37.86                21.0       7099.0         8.3014   \n",
       "2    -122.24     37.85                52.0       1467.0         7.2574   \n",
       "3    -122.25     37.85                52.0       1274.0         5.6431   \n",
       "4    -122.25     37.85                52.0       1627.0         3.8462   \n",
       "\n",
       "   CLOSE OCEAN  INLAND  NEAR BAY  NEAR OCEAN  income_per_pp  rm_per_household  \\\n",
       "0          0.0     0.0       1.0         0.0       0.025855          6.984127   \n",
       "1          0.0     0.0       1.0         0.0       0.003457          6.238137   \n",
       "2          0.0     0.0       1.0         0.0       0.014632          8.288136   \n",
       "3          0.0     0.0       1.0         0.0       0.010113          5.817352   \n",
       "4          0.0     0.0       1.0         0.0       0.006807          6.281853   \n",
       "\n",
       "   bdr_per_rm  \n",
       "0    0.146591  \n",
       "1    0.155797  \n",
       "2    0.129516  \n",
       "3    0.184458  \n",
       "4    0.172096  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create new attributes\n",
    "housing_X_oh_attr = housing_X_oh.copy()\n",
    "\n",
    "housing_X_oh_attr[\"income_per_pp\"] = housing_X_oh.median_income / housing_X_oh.population\n",
    "housing_X_oh_attr[\"rm_per_household\"] = housing_X_oh.total_rooms / housing_X_oh.households\n",
    "housing_X_oh_attr[\"bdr_per_rm\"] = housing_X_oh.total_bedrooms / housing_X_oh.total_rooms\n",
    "housing_X_oh_attr.drop([\"households\",\"population\",\"total_bedrooms\",'ISLAND'],\n",
    "                         axis=1,inplace=True)\n",
    "housing_X_oh_attr.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c3dbc3b0-62dd-4ef4-936b-bec52ac65965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attr_pearson</th>\n",
       "      <th>attr_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>0.688075</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <td>0.256617</td>\n",
       "      <td>1.131210e-307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR BAY</th>\n",
       "      <td>0.160284</td>\n",
       "      <td>7.939217e-119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm_per_household</th>\n",
       "      <td>0.151948</td>\n",
       "      <td>7.569242e-107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <td>0.141862</td>\n",
       "      <td>3.037437e-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.134153</td>\n",
       "      <td>1.689385e-83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_per_pp</th>\n",
       "      <td>0.114455</td>\n",
       "      <td>3.886117e-61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>0.105623</td>\n",
       "      <td>2.761861e-52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>-0.045967</td>\n",
       "      <td>3.923322e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>-0.144160</td>\n",
       "      <td>2.939859e-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdr_per_rm</th>\n",
       "      <td>-0.233303</td>\n",
       "      <td>3.616096e-253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INLAND</th>\n",
       "      <td>-0.484859</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    attr_pearson         attr_p\n",
       "median_income           0.688075   0.000000e+00\n",
       "CLOSE OCEAN             0.256617  1.131210e-307\n",
       "NEAR BAY                0.160284  7.939217e-119\n",
       "rm_per_household        0.151948  7.569242e-107\n",
       "NEAR OCEAN              0.141862   3.037437e-93\n",
       "total_rooms             0.134153   1.689385e-83\n",
       "income_per_pp           0.114455   3.886117e-61\n",
       "housing_median_age      0.105623   2.761861e-52\n",
       "longitude              -0.045967   3.923322e-11\n",
       "latitude               -0.144160   2.939859e-96\n",
       "bdr_per_rm             -0.233303  3.616096e-253\n",
       "INLAND                 -0.484859   0.000000e+00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attrs = []\n",
    "attr_ps = []\n",
    "for col in housing_X_oh_attr.columns:\n",
    "    attr, p = pearsonr(housing_X_oh_attr.loc[:,col],housing_y)\n",
    "    attrs.append(attr)\n",
    "    attr_ps.append(p)\n",
    "attrs = pd.DataFrame(data={\"attr_pearson\":attrs,\n",
    "                           \"attr_p\":attr_ps},\n",
    "                     index=housing_X_oh_attr.columns)\n",
    "attrs.sort_values([\"attr_pearson\"],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46557034-8631-4c1c-8de2-c5445280bb15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>js_attr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NEAR BAY</th>\n",
       "      <td>1.046660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <td>0.995093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INLAND</th>\n",
       "      <td>0.888202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_per_pp</th>\n",
       "      <td>0.419539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <td>0.379044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.156156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>0.120208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bdr_per_rm</th>\n",
       "      <td>0.115487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm_per_household</th>\n",
       "      <td>0.088259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.081588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0.077515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>0.039420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     js_attr\n",
       "NEAR BAY            1.046660\n",
       "NEAR OCEAN          0.995093\n",
       "INLAND              0.888202\n",
       "income_per_pp       0.419539\n",
       "CLOSE OCEAN         0.379044\n",
       "total_rooms         0.156156\n",
       "housing_median_age  0.120208\n",
       "bdr_per_rm          0.115487\n",
       "rm_per_household    0.088259\n",
       "latitude            0.081588\n",
       "longitude           0.077515\n",
       "median_income       0.039420"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "js_attrs = []\n",
    "for col in housing_X_oh_attr.columns:\n",
    "    attr = js_divergence(housing_X_oh_attr.loc[:,col],housing_y)\n",
    "    js_attrs.append(attr)\n",
    "js_attrs = pd.DataFrame(data={\"js_attr\":js_attrs},index=housing_X_oh_attr.columns)\n",
    "js_attrs.sort_values([\"js_attr\"],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc3f76b2-c69a-4878-9b07-ab3dce413d35",
   "metadata": {},
   "source": [
    "# training and tuning\n",
    "## random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c008e9f-af00-4dfa-a52a-5d6ae5eaf2cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['longitude' 'latitude' 'housing_median_age' 'total_rooms' 'median_income'\n",
      " 'CLOSE OCEAN' 'INLAND' 'NEAR BAY' 'NEAR OCEAN' 'income_per_pp'\n",
      " 'rm_per_household' 'bdr_per_rm']\n"
     ]
    }
   ],
   "source": [
    "print(np.array(housing_X_oh_attr.columns.to_list()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7e9ad54b-f12e-4ca7-b733-19c417ee1f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>median_income</th>\n",
       "      <th>CLOSE OCEAN</th>\n",
       "      <th>INLAND</th>\n",
       "      <th>NEAR BAY</th>\n",
       "      <th>NEAR OCEAN</th>\n",
       "      <th>income_per_pp</th>\n",
       "      <th>rm_per_household</th>\n",
       "      <th>bdr_per_rm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.327835</td>\n",
       "      <td>1.052548</td>\n",
       "      <td>0.982143</td>\n",
       "      <td>-0.804819</td>\n",
       "      <td>2.344766</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025855</td>\n",
       "      <td>0.628559</td>\n",
       "      <td>-1.029988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.322844</td>\n",
       "      <td>1.043185</td>\n",
       "      <td>-0.607019</td>\n",
       "      <td>2.045890</td>\n",
       "      <td>2.332238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003457</td>\n",
       "      <td>0.327041</td>\n",
       "      <td>-0.888897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.332827</td>\n",
       "      <td>1.038503</td>\n",
       "      <td>1.856182</td>\n",
       "      <td>-0.535746</td>\n",
       "      <td>1.782699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.014632</td>\n",
       "      <td>1.155620</td>\n",
       "      <td>-1.291686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.337818</td>\n",
       "      <td>1.038503</td>\n",
       "      <td>1.856182</td>\n",
       "      <td>-0.624215</td>\n",
       "      <td>0.932968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010113</td>\n",
       "      <td>0.156966</td>\n",
       "      <td>-0.449613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.337818</td>\n",
       "      <td>1.038503</td>\n",
       "      <td>1.856182</td>\n",
       "      <td>-0.462404</td>\n",
       "      <td>-0.012881</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.006807</td>\n",
       "      <td>0.344711</td>\n",
       "      <td>-0.639087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  median_income  \\\n",
       "0  -1.327835  1.052548            0.982143    -0.804819       2.344766   \n",
       "1  -1.322844  1.043185           -0.607019     2.045890       2.332238   \n",
       "2  -1.332827  1.038503            1.856182    -0.535746       1.782699   \n",
       "3  -1.337818  1.038503            1.856182    -0.624215       0.932968   \n",
       "4  -1.337818  1.038503            1.856182    -0.462404      -0.012881   \n",
       "\n",
       "   CLOSE OCEAN  INLAND  NEAR BAY  NEAR OCEAN  income_per_pp  rm_per_household  \\\n",
       "0          0.0     0.0       1.0         0.0       0.025855          0.628559   \n",
       "1          0.0     0.0       1.0         0.0       0.003457          0.327041   \n",
       "2          0.0     0.0       1.0         0.0       0.014632          1.155620   \n",
       "3          0.0     0.0       1.0         0.0       0.010113          0.156966   \n",
       "4          0.0     0.0       1.0         0.0       0.006807          0.344711   \n",
       "\n",
       "   bdr_per_rm  \n",
       "0   -1.029988  \n",
       "1   -0.888897  \n",
       "2   -1.291686  \n",
       "3   -0.449613  \n",
       "4   -0.639087  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "housing_X_oh_train = housing_X_oh_attr.copy()\n",
    "# normalization\n",
    "std_scaler = StandardScaler()\n",
    "housing_X_oh_train.iloc[:,:5] = std_scaler.fit_transform(housing_X_oh_train.iloc[:,:5])\n",
    "housing_X_oh_train.iloc[:,10:] = std_scaler.fit_transform(housing_X_oh_train.iloc[:,10:])\n",
    "housing_X_oh_train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6e675575-fce7-4e4c-93d1-e00d3b31b402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid=[{&#x27;colsample_bytree&#x27;: [0.5, 1], &#x27;gamma&#x27;: [0, 10],\n",
       "                          &#x27;learning_rate&#x27;: [0.01, 0.03],\n",
       "                          &#x27;max_depth&#x27;: [2, 3, 5, 10],\n",
       "                          &#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;min_child_weight&#x27;: [0, 10],\n",
       "                          &#x27;n_estimators&#x27;: [100, 200, 500], &#x27;reg_alpha&#x27;: [0, 1],\n",
       "                          &#x27;reg_lambda&#x27;: [0, 1], &#x27;subsample&#x27;: [0.5, 1]}],\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid=[{&#x27;colsample_bytree&#x27;: [0.5, 1], &#x27;gamma&#x27;: [0, 10],\n",
       "                          &#x27;learning_rate&#x27;: [0.01, 0.03],\n",
       "                          &#x27;max_depth&#x27;: [2, 3, 5, 10],\n",
       "                          &#x27;max_features&#x27;: [2, 4, 6, 8],\n",
       "                          &#x27;min_child_weight&#x27;: [0, 10],\n",
       "                          &#x27;n_estimators&#x27;: [100, 200, 500], &#x27;reg_alpha&#x27;: [0, 1],\n",
       "                          &#x27;reg_lambda&#x27;: [0, 1], &#x27;subsample&#x27;: [0.5, 1]}],\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False, eval_metric=None,\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_policy=None, importance_type=None,\n",
       "                                    interaction_constraints=None,\n",
       "                                    learning_rate=None, m...\n",
       "                                    multi_strategy=None, n_estimators=None,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid=[{'colsample_bytree': [0.5, 1], 'gamma': [0, 10],\n",
       "                          'learning_rate': [0.01, 0.03],\n",
       "                          'max_depth': [2, 3, 5, 10],\n",
       "                          'max_features': [2, 4, 6, 8],\n",
       "                          'min_child_weight': [0, 10],\n",
       "                          'n_estimators': [100, 200, 500], 'reg_alpha': [0, 1],\n",
       "                          'reg_lambda': [0, 1], 'subsample': [0.5, 1]}],\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split train set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(housing_X_oh_train,housing_y,\n",
    "                                                    shuffle=True, random_state=42,\n",
    "                                                    test_size=0.1)\n",
    "# create grid search plan\n",
    "forest_param_grid = [\n",
    "        {'n_estimators':[100,200,500],\n",
    "         'max_features':[2,4,6,8],\n",
    "         'learning_rate':[0.01,0.03],\n",
    "         'max_depth':[2,3,5,10],\n",
    "         'subsample':[0.5,1],\n",
    "         'colsample_bytree':[0.5,1],\n",
    "         'gamma':[0,10],\n",
    "         'reg_lambda':[0,1],\n",
    "         'reg_alpha':[0,1],\n",
    "         'min_child_weight':[0,10]}\n",
    "    ]\n",
    "# create model\n",
    "rf_reg = xgboost.XGBRegressor()\n",
    "# meet four reqirements of GridSearchCV\n",
    "forest_grid_search = GridSearchCV(rf_reg, forest_param_grid,\n",
    "                                 cv=5, scoring=\"neg_mean_squared_error\")\n",
    "forest_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "b1566867-7bff-4e1f-8567-2de0764e106d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'colsample_bytree': 0.5,\n",
       " 'gamma': 0,\n",
       " 'learning_rate': 0.03,\n",
       " 'max_depth': 10,\n",
       " 'max_features': 2,\n",
       " 'min_child_weight': 10,\n",
       " 'n_estimators': 500,\n",
       " 'reg_alpha': 1,\n",
       " 'reg_lambda': 1,\n",
       " 'subsample': 1}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "aa8179e0-d03d-4506-8252-b240003f8edb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=0, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.03, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=10, max_features=2, max_leaves=None,\n",
       "             min_child_weight=10, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=0, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.03, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=10, max_features=2, max_leaves=None,\n",
       "             min_child_weight=10, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=0, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.03, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=10, max_features=2, max_leaves=None,\n",
       "             min_child_weight=10, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=500, n_jobs=None,\n",
       "             num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_estimator = forest_grid_search.best_estimator_\n",
    "forest_grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c8174f0-7cd8-4a9c-b798-e78b46af51ca",
   "metadata": {},
   "source": [
    "## random forest SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "208d2aa6-7359-42e6-b8bd-d9e9ce55f70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45553.00888497143"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = np.sqrt(-forest_grid_search.best_score_)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "b0c70e40-461a-473b-8104-a9746443619a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206802.5159883721"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "5e906172-5c68-4e09-bede-72ef15ffe7c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22027299168610087"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_pct = mse / y_test.mean()\n",
    "mse_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "46a13372-0059-44ae-8e73-a57b5e1e2e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost\n",
    "import shap\n",
    "\n",
    "xgb_reg = best_estimator\n",
    "xgb_reg.fit(X_train,y_train)\n",
    "explainer = shap.Explainer(xgb_reg)\n",
    "shap_values = explainer(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "dbe9af4b-dbc4-4613-abf3-4c46b5a42b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "shap.plots.beeswarm(shap_values,max_display=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "907a1623-a418-4933-a286-2659435ce1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "shap.plots.bar(shap_values,max_display=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0628d0d-1f7f-47ae-b11c-80174c6aa6e3",
   "metadata": {},
   "source": [
    "## random forest Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6ea85dc9-e54b-4a25-9459-54666c8158f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46796.48181689552\n"
     ]
    }
   ],
   "source": [
    "y_pred = xgb_reg.predict(X_test)\n",
    "res = mean_squared_error(y_test, y_pred=y_pred,squared=False)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "d57f270b-d15b-40cd-a24d-52fdd4081fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"font.sans-serif\"] = \"SimHei\"\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "\n",
    "# y_test = y_test.to_numpy()\n",
    "residue = np.power(y_test - y_pred,2)\n",
    "\n",
    "plt.scatter(np.arange(0,len(residue),1),np.sqrt(residue),s=.8,marker=\"*\",color='red')\n",
    "plt.plot(np.arange(0,len(residue),1),np.sqrt(residue),lw=.5,ls='--',color='red',label='RMSE value')\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8915c930-04b2-46e5-aeb1-0586c07a8a90",
   "metadata": {},
   "source": [
    "## linear regression - OLS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "eb5fdd6a-a8c8-4367-b377-c96c32e7190f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>median_house_value</td> <th>  R-squared:         </th>  <td>   0.630</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                    <td>OLS</td>        <th>  Adj. R-squared:    </th>  <td>   0.630</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>              <td>Least Squares</td>   <th>  F-statistic:       </th>  <td>   2632.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>              <td>Tue, 11 Jun 2024</td>  <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                  <td>15:43:43</td>      <th>  Log-Likelihood:    </th> <td>-2.3365e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>       <td> 18576</td>       <th>  AIC:               </th>  <td>4.673e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>           <td> 18563</td>       <th>  BIC:               </th>  <td>4.674e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>               <td>    12</td>       <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>nonrobust</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>longitude</th>          <td> -5.23e+04</td> <td> 2199.867</td> <td>  -23.772</td> <td> 0.000</td> <td>-5.66e+04</td> <td> -4.8e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>latitude</th>           <td>-5.068e+04</td> <td> 2319.770</td> <td>  -21.846</td> <td> 0.000</td> <td>-5.52e+04</td> <td>-4.61e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>housing_median_age</th> <td> 1.398e+04</td> <td>  591.714</td> <td>   23.627</td> <td> 0.000</td> <td> 1.28e+04</td> <td> 1.51e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_rooms</th>        <td> 7492.6691</td> <td>  573.269</td> <td>   13.070</td> <td> 0.000</td> <td> 6369.009</td> <td> 8616.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>median_income</th>      <td> 8.216e+04</td> <td>  762.756</td> <td>  107.710</td> <td> 0.000</td> <td> 8.07e+04</td> <td> 8.37e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CLOSE OCEAN</th>        <td>-1.573e+05</td> <td> 3.14e+04</td> <td>   -5.003</td> <td> 0.000</td> <td>-2.19e+05</td> <td>-9.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>INLAND</th>             <td>-1.923e+05</td> <td> 3.15e+04</td> <td>   -6.104</td> <td> 0.000</td> <td>-2.54e+05</td> <td>-1.31e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NEAR BAY</th>           <td>-1.555e+05</td> <td> 3.15e+04</td> <td>   -4.941</td> <td> 0.000</td> <td>-2.17e+05</td> <td>-9.38e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>NEAR OCEAN</th>         <td>-1.476e+05</td> <td> 3.15e+04</td> <td>   -4.692</td> <td> 0.000</td> <td>-2.09e+05</td> <td>-8.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>income_per_pp</th>      <td>-1.899e+05</td> <td>  2.8e+04</td> <td>   -6.780</td> <td> 0.000</td> <td>-2.45e+05</td> <td>-1.35e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rm_per_household</th>   <td> 7238.2756</td> <td>  582.643</td> <td>   12.423</td> <td> 0.000</td> <td> 6096.242</td> <td> 8380.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bdr_per_rm</th>         <td> 2.253e+04</td> <td>  807.607</td> <td>   27.900</td> <td> 0.000</td> <td> 2.09e+04</td> <td> 2.41e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>              <td>  3.75e+05</td> <td> 3.14e+04</td> <td>   11.928</td> <td> 0.000</td> <td> 3.13e+05</td> <td> 4.37e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4122.002</td> <th>  Durbin-Watson:     </th> <td>   1.976</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>11670.656</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.171</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 6.098</td>  <th>  Cond. No.          </th> <td>    196.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}       & median\\_house\\_value & \\textbf{  R-squared:         } &      0.630   \\\\\n",
       "\\textbf{Model:}               &         OLS          & \\textbf{  Adj. R-squared:    } &      0.630   \\\\\n",
       "\\textbf{Method:}              &    Least Squares     & \\textbf{  F-statistic:       } &      2632.   \\\\\n",
       "\\textbf{Date:}                &   Tue, 11 Jun 2024   & \\textbf{  Prob (F-statistic):} &      0.00    \\\\\n",
       "\\textbf{Time:}                &       15:43:43       & \\textbf{  Log-Likelihood:    } & -2.3365e+05  \\\\\n",
       "\\textbf{No. Observations:}    &         18576        & \\textbf{  AIC:               } &  4.673e+05   \\\\\n",
       "\\textbf{Df Residuals:}        &         18563        & \\textbf{  BIC:               } &  4.674e+05   \\\\\n",
       "\\textbf{Df Model:}            &            12        & \\textbf{                     } &              \\\\\n",
       "\\textbf{Covariance Type:}     &      nonrobust       & \\textbf{                     } &              \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                              & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{longitude}            &    -5.23e+04  &     2199.867     &   -23.772  &         0.000        &    -5.66e+04    &     -4.8e+04     \\\\\n",
       "\\textbf{latitude}             &   -5.068e+04  &     2319.770     &   -21.846  &         0.000        &    -5.52e+04    &    -4.61e+04     \\\\\n",
       "\\textbf{housing\\_median\\_age} &    1.398e+04  &      591.714     &    23.627  &         0.000        &     1.28e+04    &     1.51e+04     \\\\\n",
       "\\textbf{total\\_rooms}         &    7492.6691  &      573.269     &    13.070  &         0.000        &     6369.009    &     8616.329     \\\\\n",
       "\\textbf{median\\_income}       &    8.216e+04  &      762.756     &   107.710  &         0.000        &     8.07e+04    &     8.37e+04     \\\\\n",
       "\\textbf{CLOSE OCEAN}          &   -1.573e+05  &     3.14e+04     &    -5.003  &         0.000        &    -2.19e+05    &    -9.57e+04     \\\\\n",
       "\\textbf{INLAND}               &   -1.923e+05  &     3.15e+04     &    -6.104  &         0.000        &    -2.54e+05    &    -1.31e+05     \\\\\n",
       "\\textbf{NEAR BAY}             &   -1.555e+05  &     3.15e+04     &    -4.941  &         0.000        &    -2.17e+05    &    -9.38e+04     \\\\\n",
       "\\textbf{NEAR OCEAN}           &   -1.476e+05  &     3.15e+04     &    -4.692  &         0.000        &    -2.09e+05    &    -8.59e+04     \\\\\n",
       "\\textbf{income\\_per\\_pp}      &   -1.899e+05  &      2.8e+04     &    -6.780  &         0.000        &    -2.45e+05    &    -1.35e+05     \\\\\n",
       "\\textbf{rm\\_per\\_household}   &    7238.2756  &      582.643     &    12.423  &         0.000        &     6096.242    &     8380.309     \\\\\n",
       "\\textbf{bdr\\_per\\_rm}         &    2.253e+04  &      807.607     &    27.900  &         0.000        &     2.09e+04    &     2.41e+04     \\\\\n",
       "\\textbf{const}                &     3.75e+05  &     3.14e+04     &    11.928  &         0.000        &     3.13e+05    &     4.37e+05     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 4122.002 & \\textbf{  Durbin-Watson:     } &     1.976  \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 11670.656  \\\\\n",
       "\\textbf{Skew:}          &   1.171  & \\textbf{  Prob(JB):          } &      0.00  \\\\\n",
       "\\textbf{Kurtosis:}      &   6.098  & \\textbf{  Cond. No.          } &      196.  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:     median_house_value   R-squared:                       0.630\n",
       "Model:                            OLS   Adj. R-squared:                  0.630\n",
       "Method:                 Least Squares   F-statistic:                     2632.\n",
       "Date:                Tue, 11 Jun 2024   Prob (F-statistic):               0.00\n",
       "Time:                        15:43:43   Log-Likelihood:            -2.3365e+05\n",
       "No. Observations:               18576   AIC:                         4.673e+05\n",
       "Df Residuals:                   18563   BIC:                         4.674e+05\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "longitude           -5.23e+04   2199.867    -23.772      0.000   -5.66e+04    -4.8e+04\n",
       "latitude           -5.068e+04   2319.770    -21.846      0.000   -5.52e+04   -4.61e+04\n",
       "housing_median_age  1.398e+04    591.714     23.627      0.000    1.28e+04    1.51e+04\n",
       "total_rooms         7492.6691    573.269     13.070      0.000    6369.009    8616.329\n",
       "median_income       8.216e+04    762.756    107.710      0.000    8.07e+04    8.37e+04\n",
       "CLOSE OCEAN        -1.573e+05   3.14e+04     -5.003      0.000   -2.19e+05   -9.57e+04\n",
       "INLAND             -1.923e+05   3.15e+04     -6.104      0.000   -2.54e+05   -1.31e+05\n",
       "NEAR BAY           -1.555e+05   3.15e+04     -4.941      0.000   -2.17e+05   -9.38e+04\n",
       "NEAR OCEAN         -1.476e+05   3.15e+04     -4.692      0.000   -2.09e+05   -8.59e+04\n",
       "income_per_pp      -1.899e+05    2.8e+04     -6.780      0.000   -2.45e+05   -1.35e+05\n",
       "rm_per_household    7238.2756    582.643     12.423      0.000    6096.242    8380.309\n",
       "bdr_per_rm          2.253e+04    807.607     27.900      0.000    2.09e+04    2.41e+04\n",
       "const                3.75e+05   3.14e+04     11.928      0.000    3.13e+05    4.37e+05\n",
       "==============================================================================\n",
       "Omnibus:                     4122.002   Durbin-Watson:                   1.976\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            11670.656\n",
       "Skew:                           1.171   Prob(JB):                         0.00\n",
       "Kurtosis:                       6.098   Cond. No.                         196.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tools import add_constant\n",
    "\n",
    "# shuffle \n",
    "df_ols = pd.concat([housing_X_oh_train,housing_y],axis=1)\n",
    "indicator = np.arange(0,len(df_ols),1)\n",
    "np.random.shuffle(indicator)\n",
    "train_ind = indicator[:int(0.8*len(indicator))]\n",
    "test_ind = indicator[int(0.8*len(indicator)):]\n",
    "train = df_ols.iloc[train_ind,:]\n",
    "test = df_ols.iloc[test_ind,:]\n",
    "\n",
    "X_train_ols = X_train.copy()\n",
    "X_train_ols = sm.add_constant(X_train_ols,prepend=False)\n",
    "ols_model_1 = sm.OLS(endog=y_train,exog=X_train_ols)\n",
    "ols_res_1 = ols_model_1.fit()\n",
    "ols_res_1.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "11416878-047f-4c52-9910-ade672bd9b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             -52296.041246\n",
       "latitude              -50676.575939\n",
       "housing_median_age     13980.419394\n",
       "total_rooms             7492.669090\n",
       "median_income          82156.787410\n",
       "CLOSE OCEAN          -157285.754166\n",
       "INLAND               -192339.409779\n",
       "NEAR BAY             -155543.852919\n",
       "NEAR OCEAN           -147568.331041\n",
       "income_per_pp        -189902.982664\n",
       "rm_per_household        7238.275614\n",
       "bdr_per_rm             22532.108056\n",
       "const                 375012.968711\n",
       "dtype: float64"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ols_res_1.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "16c95cac-703e-4791-a6fb-69ca303411d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -52296.04124562,  -50676.57593869,   13980.41939354,\n",
       "          7492.66908996,   82156.78741023, -157285.75416555,\n",
       "       -192339.40977927, -155543.85291887, -147568.3310408 ,\n",
       "       -189902.98266357,    7238.27561384,   22532.10805597,\n",
       "        375012.9687108 ])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = np.array(ols_res_1.params)\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "e72560c7-493d-489f-8c62-3166203f87b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3934889713747413\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "\n",
    "X_test_ols = X_test.copy()\n",
    "X_test_ols = sm.add_constant(X_test_ols,prepend=False)\n",
    "y_pred_ols = np.dot(X_test_ols, params.T)\n",
    "mse_ols_1 = mean_squared_error(y_test, y_pred_ols)\n",
    "print(math.sqrt(mse_ols_1)/y_test.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9662887-af5d-4097-8bcb-491c2d2471c4",
   "metadata": {},
   "source": [
    "## decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6945e517-d4a7-4e34-a138-ed96aa87a9d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1590211548521057\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "\n",
    "dt_reg = DecisionTreeClassifier(max_depth=3, min_samples_leaf=0.25)\n",
    "dt_reg.fit(X_train,y_train)\n",
    "y_pred_dt = dt_reg.predict(X_test)\n",
    "mse_dt = mean_squared_error(y_test,y_pred_dt)\n",
    "print(math.sqrt(mse_dt) / y_test.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81003926-c5ca-4adb-8fed-a1cf42ae2061",
   "metadata": {},
   "source": [
    "## decision tree SHAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f30702ef-3ab9-44b3-86b9-d38e75e1e3d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings \n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "explainer_dt = shap.Explainer(dt_reg)\n",
    "shap_values_dt = explainer(X_train)\n",
    "shap.plots.beeswarm(shap_values_dt,max_display=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "1acac39e-cf54-42e6-b37a-bb52e005feb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings \n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "shap.plots.bar(shap_values_dt,max_display=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "371b404f-d767-4719-a8f0-6cc6dc8f4c69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
